Advanced breast cancer incidence following population-based mammographic screening

Background: Breast cancer mortality is declining in many Western countries. If mammography screening contributed to decreases in mortality, then decreases in advanced breast cancer incidence should also be noticeable.
Patients and methods: We assessed incidence trends of advanced breast cancer in areas where mammography screening is practiced for at least 7 years with 60% minimum participation and where population-based registration of advanced breast cancer existed. Through a systematic Medline search, we identified relevant published data for Australia, Italy, Norway, Switzerland, The Netherlands, UK and the USA. Data from cancer registries in Northern Ireland, Scotland, the USA (Surveillance, Epidemiology and End Results (SEER), and Connecticut), and Tasmania (Australia) were available for the study. Criterion for advanced cancer was the tumour size, and if not available, spread to regional/distant sites.
Results: Age-adjusted annual percent changes (APCs) were stable or increasing in ten areas (APCs of -0.5% to 1.7%). In four areas (Firenze, the Netherlands, SEER and Connecticut) there were transient downward trends followed by increases back to pre-screening rates.
Conclusions: In areas with widespread sustained mammographic screening, trends in advanced breast cancer incidence do not support a substantial role for screening in the decrease in mortality.

Sustained release of proteins from a modified vaginal ring device

A new vaginal ring technology, the insert vaginal ring (InVR), is presented. The InVR overcomes the current shortfall of conventional vaginal rings (VRs) that are generally ineffectual for the delivery of hydrophilic and/or macromolecular actives, including peptides, proteins and antibodies, due to their poor permeation characteristics in the hydrophobic polymeric elastomers from which VRs are usually fabricated. Release of the model protein BSA from a variety of insert matrices for the InVR is demonstrated, including modified silicone rods, directly compressed tablets and lyophilised gels, which collectively provided controlled release profiles from several hours to beyond 4 weeks. Furthermore, the InVR was shown to deliver over 1 mg of the monoclonal antibody 2F5 from a single device, offering a potential means of protecting women against the transmission of HIV.

Can China’s Growth be Sustained? A Productivity Perspective

China’s gradual approach to economic transition has resulted in sustained high growth. However, in recent years Chinese economists have increasingly referred to the growth pattern as “extensive,” generated mainly through the expansion of inputs. Our investigation of the Chinese economy during the reform period finds that reform measures often resulted in one-time level effects on total factor productivity (TFP). China now needs to adjust its reform program toward sustained increases in productivity. Market and ownership reforms, and open door policies have improved the conditions under which Chinese firms operate, but further institutional reforms are required to consolidate China’s move to a full-fledged market economy.

Improved Nonlinear PCA for Process Monitoring Using Support Vector Data Description

Nonlinear principal component analysis (PCA) based on neural networks has drawn significant attention as a monitoring tool for complex nonlinear processes, but there remains a difficulty with determining the optimal network topology. This paper exploits the advantages of the Fast Recursive Algorithm, where the number of nodes, the location of centres, and the weights between the hidden layer and the output layer can be identified simultaneously for the radial basis function (RBF) networks. The topology problem for the nonlinear PCA based on neural networks can thus be solved. Another problem with nonlinear PCA is that the derived nonlinear scores may not be statistically independent or follow a simple parametric distribution. This hinders its applications in process monitoring since the simplicity of applying predetermined probability distribution functions is lost. This paper proposes the use of a support vector data description and shows that transforming the nonlinear principal components into a feature space allows a simple statistical inference. Results from both simulated and industrial data confirm the efficacy of the proposed method for solving nonlinear principal component problems, compared with linear PCA and kernel PCA.